Influence of Traffic Signal Timing on Red-Light Running and Potential Vehicle Conflicts at Urban Intersections

1997 ◽  
Vol 1595 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Richard A. Retting ◽  
Michael A. Greene
Keyword(s):  
Urban Areas ◽  
Motor Vehicle ◽  
Red Light ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Signal Timing ◽  
Red Light Running ◽  
Sustained Effects ◽  
Traffic Violation ◽  
Traffic Signal Timing

Motor vehicle crashes at traffic signals are a major source of injuries and property damage, especially in urban areas. Many crashes result from vehicles entering the intersection after the onset of a red light, a traffic violation that may be affected by the duration of the change interval (the yellow and all-red periods of the traffic signal). The purpose of this study was to examine short-term and sustained effects on red-light compliance and potential vehicle conflicts as a result of an increase in change intervals to values associated with the Institute of Traffic Engineers (ITE) proposed recommended practice for determining vehicle change intervals. Data were collected during an experiment in an urban location involving changes in signal timing at some 10 intersections. Observations included the proportion of signal cycles with vehicles entering on a red light and the proportion of vehicles exiting the intersection after the onset of a conflicting green signal. Results indicate that change intervals set closer to ITE’s proposed recommended practice can reduce red-light violations and potential right-angle vehicle conflicts and that such safety benefits can be sustained.

Red-Light Running and Sensible Countermeasures: Summary of Research Findings

1998 ◽  
Vol 1640 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Richard A. Retting ◽  
Allan F. Williams ◽  
Michael A. Greene
Keyword(s):  
Motor Vehicle ◽  
Red Light ◽  
Traffic Signals ◽  
Signal Timing ◽  
Motor Vehicle Collisions ◽  
Red Light Running ◽  
Vehicle Collisions ◽  
Red Light Cameras ◽  
Safety Research ◽  
Research Findings

Deliberate running of red lights is a common and serious violation that contributes substantially to the more than 1 million motor vehicle collisions that occur at traffic signals each year. Urban-based highway safety research has examined various aspects of the red-light running problem, including the contribution of red-light violations to motor vehicle crashes, the frequency of red-light running, characteristics of red-light runners, and the influence of signal timing on red-light running behavior. A brief summary of recent research efforts to examine the problem of red-light running is provided, and the use of countermeasures, including red-light cameras, to reduce the problem is discussed.

scholarly journals Multi-Agent Reinforcement Learning for Optimizing Traffic Signal Timing

2021 ◽  
Author(s):  
Areej Salaymeh ◽  
Loren Schwiebert ◽  
Stephen Remias
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signals ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Model Free ◽  
Proposed Model ◽  
Multi Agent ◽  
Traffic Signal Timing

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network. In this paper, a model-free approach is used to optimize signal timing for complicated multiple four-phase signalized intersections. We propose a multi-agent deep reinforcement learning framework that aims to optimize traffic flow using data within traffic signal intersections and data coming from other intersections in a Multi-Agent Environment in what is called Multi-Agent Reinforcement Learning (MARL). The proposed model consists of state-of-art techniques such as Double Deep Q-Network and Hindsight Experience Replay (HER). This research uses HER to allow our framework to quickly learn on sparse reward settings. We tested and evaluated our proposed model via a Simulation of Urban MObility simulation (SUMO). Our results show that the proposed method is effective in reducing congestion in both peak and off-peak times.

Traditional Traffic Demand Definition Defects Analyze and Redefinition

2011 ◽  
Vol 255-260 ◽  
pp. 4061-4064
Author(s):  
Yu Guang Chen ◽  
Wei Cheng ◽  
Hai Cheng Xiao
Keyword(s):  
Red Light ◽  
Traffic Signal ◽  
Signal Timing ◽  
Effective Demand ◽  
Traffic Demand ◽  
The Real ◽  
Traffic Signal Timing ◽  
Real Traffic ◽  
Stop Line

Traditional traffic signal timing theory default the number of vehicles through the stop line per unit time as traffic demand. The traditional traffic demand definition obviously cannot respond the real traffic demand when the intersection is oversaturated. The main objective of this paper includes the following contents: (1) to analyze the defects of the traditional traffic demand definition; (2) to strictly define the concept of traffic demand. To achieve the research objective, researchers propose the “traffic demand in cycle” definition: the summation of the standard vehicles queuing during the red light time and the standard vehicles reaching the effective demand region during the non-red light time. The new definition introduces the concept of “effective demand areas”, referring to the areas follow-up vehicles slow down affected by the vehicles that haven’t pass the stop line. The new definition developed in the study can be directly used by traffic engineers and managers to signal timing.

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yangsheng Jiang ◽  
Bin Zhao ◽  
Meng Liu ◽  
Zhihong Yao
Keyword(s):  
Random Environment ◽  
Arrival Time ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Signal Timing ◽  
Automated Vehicles ◽  
Gasoline Consumption ◽  
Level Model ◽  
Traffic Signal Timing ◽  
Two Level Model

Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.

scholarly journals The role of optimization to control traffic signals setting on capacity and flow at peak hours at intersections

2015 ◽  
Vol 4 (3) ◽  
pp. 432
Author(s):  
Pegah Jafari Haghighatpour ◽  
Ali Mansourkhaki ◽  
Gholamreza Mehdizadeh ◽  
Mahmouddreza Keymanesh
Keyword(s):  
Traffic Congestion ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Control Measures ◽  
Modular System ◽  
Signal Timing ◽  
Peak Time ◽  
Central System ◽  
Traffic Signal Timing ◽  
Freeway Networks

Control the pre timing of traffic signals has many advantages because of its responsiveness to traffic demands, short cycles, and effective use of capacity leading to and recovering from oversaturation and amenability to aggressive transit priority. Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. Coordination between intersections in a central system increases capacity, appropriate traffic flow and reduce total delays of vehicles. Scats system has been installed in many world intersections, and many researchers have done about the advantages of it to decrease delays and travel times at intersection. SCATS is a modular system and development of this is possible. Now in Tehran's this system is used and traffic conditions matches on it. Traffic signal timing schedule gives in this system as default and although this system is capable of adapting to the moment traffic but in terms of super saturation and during peak hours due to the lack of optimization, default program around intersection has caused widespread congestion at intersection. In this paper, two intersections of East and West of Tehran have been selected, and in two different situations, the flow to capacity ratio of traffic signal timing has been investigated before and after optimization. Simulation by AIMSUN and optimization by SYNCHRO software is done. After optimization can be observed that if before the pre-defined schedule for SCATS, this plan for each intersection of the volume of traffic at peak hours has been optimized, a great reduction in delay and increase in capacity can be observed at intersections. For example, delay time reduction has been occurred about 14.77 in AM peak time and 12.65 in PM peak time at GOLBARG-DARDASHT intersection.

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